Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Contrastive Speech Analytics and temporal Flaw Grounding

AI-Powered Speech Evaluation System

Inspiration

We got the idea for this project because evaluating a speech is not always easy. Different judges may give different scores to the same speaker. Sometimes, speakers lose marks for their tone, speed, pronunciation, or unnecessary pauses, but they may not know exactly where they went wrong. So, we decided to build a system that can analyze speech and help people understand their mistakes.

How We Built the Project

We are developing a speech evaluation system using Python and audio-processing techniques. The system compares good speech recordings with recordings that contain delivery mistakes, while keeping the spoken content the same. We extract audio features such as pitch, speaking rate, pauses, and MFCCs to understand how the speech is delivered.

Our goal is to create a dashboard where users can view their speech scores, identify the parts that need improvement, and receive useful suggestions to improve their speaking skills.

What We Learned

While working on this project, we learned how to process audio recordings, extract speech features, and use machine learning techniques to compare different speaking styles. We also learned that even small changes in tone, speed, and pauses can affect how a speech sounds. This project helped us understand how AI can be used to solve real-world problems.

Challenges We FacedOne of the main challenges was finding a way to compare good and flawed speech fairly. We also faced difficulties in identifying the exact parts of a recording where delivery mistakes occurred. Extracting useful features and matching the speech with the correct timestamps required careful work. We are working to overcome these challenges by combining different audio features and comparing each recording with a good speech baseline.

Future Scope

In the future, we would like to improve the accuracy of our system, support multiple languages, and provide more detailed feedback. We also plan to improve the dashboard so users can easily understand their results and track their progress over time.

Conclusion

Through this project, we hope to make speech evaluation easier and more useful for students and public speakers. Our aim is not just to give scores, but to help people understand their mistakes and become more confident speakers.

Built With

• ai
• artificial
• audio
• code
• contrastive
• css
• frameworks
• git
• github
• html
• intelligence
• javascript
• language
• languages
• learning
• machine
• processing
• programming
• python
• signal
• speech
• studio
• visual